The proposed investigations will elucidate the neural mechanisms underlying the acquisition and representation of speaking skills and other motor learning tasks. Functional magnetic resonance imaging (fMRI) will be used to identify experience-dependent changes in functional activation of brain regions involved in learning an association between the movements of the speech articulators and changes in the frequency/location of a tracking stimulus. An examination of functional activation patterns following subjects' mastery of the task will provide insights into the representation of learned sensory-motor mappings for speech articulatory control. These findings will be used to address several issues regarding the learning and control of goat-directed articulator movements. The identification of statistical correlations between performance measures of learning (e.g., decreases in motor error and experience-dependent patterns of functional activation) will be used to identify brain regions associated with detecting and correction of articulatory motor errors, as well as regions involved in formation and storage of an internal model of the sensory-motor mappings for control of the speech articulators. A between-group comparison of functional activations during auditory vs. visual tracking stimuli will address whether and to what extent the neural processes underlying articulatory motor planning are influenced by the sensory modality of the target space. The random introduction of unanticipated delays in the responsiveness of the tracking stimulus will be used to identify neural structures responsible for processing sensory feedback and detecting discrepancies between predicted and actual feedback. Magnetoencephalography (MEG) will supplement fMRI findings by identifying the temporal sequence of cortical responses to changes in the position/frequency of the tracking stimulus. The results of these analyses will be used to test specific predictions of the DIVA model of speech acquisition and production (Guenther et al., 1995; 1998; in press). Experimental results will be compared to computer simulations of model's learning processes, and any discrepancies between the model and data will be used to guide revisions of the model.